AI Agents in Business - What Sets Them Apart from Other Technologies?

This article is part of a series on business applications of AI agents, where we explore the characteristics, use cases, and strategic utilization of AI agents in organizations.

What if most of your organization's daily decision-making and routine work could be handled without anyone even requesting it to happen? This isn't a vision of the future—this is reality today. Bank of America's AI agent Erica has processed over 2.5 billion interactions with customers, serving more than 20 million clients [1]. Airbus's Skywise Fleet Performance+ agent now monitors 11 600+ commercial aircraft; an airline using the system avoided 120 flight cancellations, 500 delays and €9 million in indirect costs during 2024 [2]. This is no longer about simple robotics, but autonomous systems—AI agents.

AI technologies have developed at a tremendous pace in recent years, but the rise of AI agents represents a fundamental shift in business automation. While the term "AI agent" may be new or unclear to decision-makers, these technologies are already fundamentally transforming industries from customer service to financial advisory and supply chain management. Their most significant difference from traditional automation is their ability to make independent decisions, learn from experience, and act proactively to achieve organizational goals.

In this article, I'll examine what AI agents are and how they differ from other technologies such as AI assistants and traditional automation. Additionally, I'll explore how they compare to human experts and what collaboration models can be built between them. In later articles in this series, I'll focus on concrete use cases across different industries, as well as implementation and integration strategies for agent technologies.

Core Capabilities of AI Agents

An AI agent is a system capable of operating independently or semi-autonomously in its environment to fulfill defined objectives. Unlike passive AI models that only respond to given requests, an agent observes, plans, and actively operates in its environment.

Key characteristics of AI agents include:

Autonomy

Agents operate independently without continuous guidance, making decisions within the framework of given objectives and constraints. Example: Airbus's Skywise Fleet Performance+ continuously analyses flight-health data across more than 11 000 aircraft [2].

Observation

Agents collect information from their environment through various interfaces and tools. Example: Mastercard's Decision Intelligence Pro agent now analyses each purchase in under 50 milliseconds, drawing on trillions of data points to help issuers approve or block the network's 143 billion yearly card transactions - cutting false declines by 50 percent [3].

Planning

Agents can create plans to achieve their goals and adapt them as needed. Example: Ocado's Hive swarm-robot agent orchestrates thousands of pick-and-pack moves in real time; its fleet can assemble a 50-item grocery order in just five minutes [4].

Tool Use

Agents can utilize various tools and interfaces to perform tasks, such as search services, databases, calculation capabilities, or other software. Examples: Intuit's generative-AI Intuit Assist turns emails or photos of notes directly into QuickBooks invoices and drafts personalised reminders—beta users were paid five days faster on average [10]. Microsoft 365 Copilot can generate a full recap (key points, action items, deadlines) for a missed Teams meeting in about 11 minutes—nearly 4 times faster than the 42 minutes it took without Copilot [5].

Learning

The most advanced agents learn from their experiences and improve their performance over time. Example: Spotify's AI DJ acts as a learning agent - reading real-time listening context and autonomously curating tracks, commentary, and mood shifts; the feature continuously refreshes the lineup based on each skip or like [6].

These characteristics make AI agents particularly suitable for various business tasks, such as customer service, risk assessment, system monitoring, and supply chain optimization.

Differences Between AI Agents and AI Assistants

AI agents are often confused with AI assistants, but there are significant differences between them. Traditional AI assistants, such as chatbots in their basic form, operate mainly reactively, responding to user requests and performing simple tasks. They typically require continuous human guidance and interaction.

AI agents, on the other hand:

1. Perform tasks independently and proactively
2. Can manage more complex processes
3. Make decisions within given parameters
4. Are capable of planning and prioritizing their actions
5. Can use multiple different tools to complete tasks

This can be illustrated with a workplace analogy: An AI assistant is like a personal assistant who is always ready to help when asked—it responds to emails, organizes calendars, and takes notes according to your requests, but always waits for your initiative and guidance.

An AI agent, on the other hand, is like an independent project manager to whom you give project goals and a timeline, after which it handles the project execution independently: it coordinates resources, makes decisions when encountering problems, communicates with necessary parties, and reports to you only in critical situations or when the project is complete. The agent doesn't need continuous guidance for every detail but works proactively to achieve goals.

Case Example: The Same Task with an Assistant and an Agent

Task: Planning and implementing a marketing campaign

AI Assistant's approach:

• User asks the assistant to draft a marketing message
• Assistant produces the message according to given instructions
• User asks the assistant to find suitable target groups
• Assistant suggests target groups based on previous data
• User asks the assistant to schedule the message delivery
• Assistant does the scheduling according to given parameters
• User requests a report on campaign results
• Assistant compiles the report according to given instructions

AI Agent's approach:

• User defines a goal for the agent: "Design and implement a marketing campaign that increases product X sales by 15% over the next month."
• Agent analyzes previous campaigns and market data to identify the most effective approaches
• Agent independently develops a campaign strategy, including messages, target groups, and timing
• Agent implements the campaign automatically, optimizing it in real-time based on results
• Agent monitors results and makes corrective actions as needed
• Agent reports campaign results and learnings to the user upon completion

Differences Between AI Agents and Traditional Automation

There are significant differences between AI agents and traditional automation. While automation has been central to process optimization for decades, AI agents represent the next step toward truly autonomous systems. Understanding the differences between these two approaches helps us better grasp when to utilize each and how they can complement each other.

Agent Workflow Management—A Key Difference from Automation

One of the most significant differences between AI agents and traditional automation is their ability to manage complex workflows. Traditional automation performs individual tasks or simple, predefined workflows. AI agents, on the other hand, can manage multi-stage, adaptive processes that may require using multiple systems, making decisions under uncertainty, and adapting in real-time to changing conditions.

Example from healthcare: Counterpart Health leverages AI agents to optimize healthcare workflows. Their Counterpart Assistant software enables a generative AI search experience across a patient's entire digital health record at the point of care. This allows clinicians to quickly access critical insights synthesized from more than 100 integrated data sources—such as recent tests, hospital visits, and medication adherence—to support early diagnosis and effective chronic disease management [7].

Unlike traditional search systems, Counterpart's AI agent doesn't just search for information, but also understands clinical context, ranks findings by relevance and fuses 100+ data sources into one view. This demonstrates how AI agents can manage significantly more complex workflows than traditional automation, especially in situations requiring contextual understanding and information integration from multiple sources.

Key Differences by Business Area

The differences between automation and AI agents manifest differently across business areas:

Customer Service and User Experience:

• Traditional automation: Automated email confirmations, simple chatbots, use of prepared answers
• AI agents: Customer service agents understanding conversation context, personalized product recommendations, systems anticipating customer needs

Financial Advisory and Management:

• Traditional automation: Automatic bank transfers, scheduled payments, simple tax reminders
• AI agents: Personalized financial advice adapting to the customer's changing situation, proactive identification of savings opportunities

Real-time Decision Making and Risk Management:

• Traditional automation: Simple rule-based fraud detection, predefined risk thresholds
• AI agents: Dynamic fraud prevention learning new scam patterns; contextual risk assessment adapting to circumstances

System Monitoring and Troubleshooting:

• Traditional automation: Scheduled backups, simple monitoring alerts, fixed maintenance schedules
• AI agents: Predictive fault detection and repair, system self-optimization, adaptive maintenance scheduling

Supply Chain Management and Logistics:

• Traditional automation: Production line scheduling, quantity-based replenishment orders, quality control checklists
• AI agents: Adaptive production planning, dynamic route planning, demand forecasting and inventory management

Healthcare and Medicine:

• Traditional automation: Automatic appointment confirmation, simple reminders, routine patient data recording
• AI agents: Continuous patient health monitoring and predictive healthcare, personalized treatment plans, diagnostic support for clinicians

Combining Automation and AI Agents

Although AI agents represent more advanced technology, they don't replace traditional automation but complement it. In many cases, the optimal solution is to combine both approaches:

1. Layered approach: Basic automation handles simple and repetitive tasks, while AI agents handle more complex decisions and exceptions.
2. Intelligent control of automation: AI agents can guide and optimize traditional automation systems, determining, for example, when and how automation is applied.
3. Hybrid solution: Different stages of a process can apply either automation or AI agents according to their strengths.
4. Evolutionary approach: Start with simpler automation and gradually move toward smarter agents as the process and systems evolve.

AI Agents vs. Human Experts

Comparing AI agents and human experts helps understand how they can complement each other across different business areas. It's not about replacement but optimal division of labor, where both strengths can shine.

AI Agents:

• Speed: Process vast amounts of data and perform tasks considerably faster than humans
• Scalability: Can serve an unlimited number of customers or monitor numerous systems simultaneously
• Consistency: Operate with the same logic and precision, without getting tired or frustrated
• Data processing: Able to analyze and combine enormous amounts of data from different sources
• Availability: Operate 24/7 without breaks or vacation periods
• Limitations: Limited creativity, intuition, and empathy; difficulties in completely new situations

Human Experts:

• Intuition: Can utilize tacit knowledge and experience-based intuition
• Creativity: Capable of innovative, lateral thinking and developing new solutions
• Empathy: Understand emotional and social nuances, build trust and relationships
• Adaptability: Adapt quickly to completely new, unprecedented situations
• Comprehensive view: Understand broad contexts and can combine knowledge from different fields
• Limitations: Cognitive biases, fatigue, limited resources, slow knowledge updates

When are AI agents more effective than human experts?

AI agents are particularly effective in situations where:

1. Repetitive decisions - Ocado's Hive swarm-robot agent orchestrates thousands of pick-and-pack moves in real time; its fleet can assemble a 50-item grocery order in just five minutes [4].
2. Rapid response - Mastercard's Decision Intelligence Pro agent analyses each purchase in under 50 milliseconds, drawing on trillions of data points to help issuers approve or block the network's 143 billion yearly card transactions - cutting false declines by 50 percent [3].
3. Vast and complex data volumes - Spotify's AI DJ processes each listener's historical and in-session behaviour in real time to keep the music stream relevant without manual searching or playlist editing [6].
4. Heavy documentation workloads - Suki Assistant's ambient agent reduces clinical note-taking time by 72 %, giving clinicians the time back for direct patient care [8].

When are human experts more effective than AI agents?

Human experts are irreplaceable in situations where:

1. Creative problem-solving is required: In product development and innovation processes, humans' lateral thinking and ability to combine expertise from different fields produces solutions that algorithms can't yet develop. (Though the recently released OpenAI's o3 language model is said to be capable of developing innovative ideas.)
2. Emotional intelligence is important: In demanding negotiation situations or crisis communication, humans' ability to interpret subtle emotional cues and adapt to them is superior.
3. Encountering completely unprecedented situations: When facing a situation with no previous data or experience, humans' intuition and ability to apply knowledge to new contexts is crucial.

Collaboration Models Between AI Agents and Humans

In the future, the most successful organizations will find the optimal way to combine AI agents and human experts. Here are some common collaboration models:

1. Tiered Model: AI agents handle routine and simpler cases, escalating more complex situations to human experts.
2. Coach Model: Human experts act as coaches for AI agents, teaching and correcting them when needed, enabling continuous development.
3. Assistant Model: AI agents serve as assistants to human experts, gathering and analyzing data, suggesting actions, but leaving final decisions to humans.
4. Parallel Model: AI agents and humans work in parallel on the same problems, leveraging each other's strengths and offering different perspectives.
5. Hybrid Task Division: Tasks are divided according to natural strengths—data-intensive tasks for agents, tasks requiring creativity and empathy for humans.

Choosing the Right Collaboration Model

The choice of optimal collaboration model depends on several factors:

1. Task complexity and nature: The more complex and ambiguous the task, the stronger role humans should have. For example, in creative design, the human component is critical, while in data analysis, AI agents can take a larger role.
2. Risk level and decision criticality: In high-risk decisions, such as in healthcare or financial markets, a tiered or assistant model offers humans the opportunity to oversee critical decisions.
3. Available data and history: If high-quality historical data is available from similar situations, AI agents can take a more independent role.
4. Need for emotional intelligence: In situations requiring empathy and understanding of human relationships, the human role is irreplaceable.
5. Need for scalability: If a process must scale significantly, the role of AI agents should be increased, but maintaining human oversight in some form.

Effective utilization of AI agents doesn't mean replacing human experts, but refocusing their work on tasks where human expertise, creativity, and empathy bring the most added value. This shift can also make many job roles more meaningful when routine and repetitive parts are automated.

Looking Ahead: From Single Agents to Multi-Agent Systems

Tomorrow's frontier is multi-agent systems (MAS) — specialised agents that negotiate, delegate and verify each other's work. Deloitte's year-end State of Generative AI in the Enterprise survey (Q4 2024, N = 2 773 leaders) found that 45 % of organisations are already exploring multi-agent systems as a key GenAI investment area [9]. Part 3 of this series will unpack the architectures and early pilots behind that momentum.

Summary

AI agents represent a significant step forward in organizations' ability to automate complex processes and make data-driven decisions independently. Unlike traditional automation systems that operate according to predefined rules, AI agents learn, adapt, and act proactively to achieve goals.

In this article, we have covered:

• Basic characteristics of AI agents, such as autonomy, observation, planning, tool use, and learning
• Key differences between AI agents and AI assistants in operating methods and use cases
• Key differences between AI agents and traditional automation across different business areas
• Strengths and limitations of AI agents and human experts in various tasks
• Different collaboration models between AI agents and human experts
• The potential of multi-agent systems as a future development direction

Understanding AI agents as part of the technology ecosystem helps organizations design sensible automation strategies where the right technology is targeted for the right purpose. Agents don't replace other technologies or human experts but complement them by bringing new capabilities to organizations.

In the next article in the series, we will look into concrete cases where AI agents have been successfully utilized across different industries. We'll examine real use cases from customer service to risk management and production optimization, as well as the measurable business benefits associated with them.

Points to Consider

• In which business areas of your organization could you benefit from AI agents' ability to act independently and proactively?
• Which current automation solutions could benefit from the adaptability and learning capability of AI agents?
• Which tasks in your organization still strongly require human creativity, intuition, and empathy?
• What kind of collaboration model between agents and humans would best suit your organization's operations and culture?
• In which business areas could multi-agent systems produce particular added value compared to current solutions?

References

1. Bank of America. (2025). "Digital Interactions by BofA Clients Surge to Over 26 Billion, up 12% Year-Over-Year". Press Release. https://newsroom.bankofamerica.com/content/newsroom/press-releases/2025/02/digital-interactions-by-bofa-clients-surge-to-over-26-billion--u.html
2. Airbus. (2024, December). Commercial Aircraft Presentation – December 2024 (slide 20). https://www.airbus.com/sites/g/files/jlcbta136/files/2024-12/Commercial-corporate-presentation_EN_December_2024.pdf
3. Payment Expert. (2024, Feb 2). "Mastercard enhances Decision Intelligence tool in latest AI move." https://paymentexpert.com/2024/02/02/mastercard-fraud-prevention-ai/
4. Ocado Group. (2024, Sept 18). "Our Technology – The Hive." https://www.ocadogroup.com/solutions/our-technology
5. Microsoft WorkLab. (2024, Mar 6). "What Copilot's earliest users teach us about AI at work." https://www.microsoft.com/en-us/worklab/work-trend-index/copilots-earliest-users-teach-us-about-generative-ai-at-work
6. Spotify Newsroom. (2023-02-22). "Spotify Debuts a New AI DJ, Right in Your Pocket." https://newsroom.spotify.com/2023-02-22/spotify-debuts-a-new-ai-dj-right-in-your-pocket/
7. Google Cloud. (2025). "How healthcare organizations are using generative AI search and agents". Google Cloud Blog. https://blog.google/products/google-cloud/himss-2025/
8. Suki AI. (2025, Apr 3). "Suki unveils industry-first ambient orders staging for its AI Assistant – reducing documentation time by 72 percent." https://www.suki.ai/news/suki-unveils-industry-first-ambient-orders-staging-for-its-ai-assistant/
9. Deloitte. (2025, Jan.). "State of Generative AI in the Enterprise, Q4 2024 – Now Decides Next." Deloitte AI Institute. https://www2.deloitte.com/us/en/pages/consulting/articles/state-of-generative-ai-in-enterprise.html
10. Intuit QuickBooks. (2024, Dec 12). "What's new in QuickBooks Online – December 2024: Introducing Intuit Assist." https://quickbooks.intuit.com/r/product-update/whats-new-quickbooks-online-december-2024/